Various face image datasets intended for facial biometrics research were
created via web-scraping, i.e. the collection of images publicly available on
the internet. This work presents an approach to detect both exactly and nearly
identical face image duplicates, using file and image hashes. The approach is
extended through the use of face image preprocessing. Additional steps based on
face recognition and face image quality assessment models reduce false
positives, and facilitate the deduplication of the face images both for intra-
and inter-subject duplicate sets. The presented approach is applied to five
datasets, namely LFW, TinyFace, Adience, CASIA-WebFace, and C-MS-Celeb (a
cleaned MS-Celeb-1M variant). Duplicates are detected within every dataset,
with hundreds to hundreds of thousands of duplicates for all except LFW. Face
recognition and quality assessment experiments indicate a minor impact on the
results through the duplicate removal. The final deduplication data is publicly
available.

通过使用文件和图像哈希的方法，结合人脸图像预处理，检测相同和近似的人脸图像重复，进一步基于人脸识别和图像质量评估模型，减少误报和便于去重处理，该方法在多个数据集上验证并公开了去重数据。

面部图像数据集中的重复样本：影响和检测

Double Trouble? Impact and Detection of Duplicates in Face Image  Datasets

Age estimation of face images is a crucial task with various practical
applications in areas such as video surveillance and Internet access control.
While deep learning-based age estimation frameworks, e.g., convolutional neural
network (CNN), multi-layer perceptrons (MLP), and transformers have shown
remarkable performance, they have limitations when modelling complex or
irregular objects in an image that contains a large amount of redundant
information. To address this issue, this paper utilizes the robustness property
of graph representation learning in dealing with image redundancy information
and proposes a novel Masked Contrastive Graph Representation Learning (MCGRL)
method for age estimation. Specifically, our approach first leverages CNN to
extract semantic features of the image, which are then partitioned into patches
that serve as nodes in the graph. Then, we use a masked graph convolutional
network (GCN) to derive image-based node representations that capture rich
structural information. Finally, we incorporate multiple losses to explore the
complementary relationship between structural information and semantic
features, which improves the feature representation capability of GCN.
Experimental results on real-world face image datasets demonstrate the
superiority of our proposed method over other state-of-the-art age estimation
approaches.

本文提出了一种基于掩蔽对比图表示学习的年龄估计方法，该方法利用图表示学习的鲁棒性来处理图像冗余信息，通过深度学习的方法从面部图像数据集中提取结构信息和语义特征，并在多个损失函数的约束下综合利用二者提高预测精度。